AI factory needs range from enterprises just starting their generative AI journey with sixteen GPUs to highly sophisticated organizations with decades of HPC experience now using AI to transform HPC.
Thierry Pienaar explains that AI factory deployments span a maturity spectrum, from small generative-AI newcomers to long-time HPC shops now transforming their operations with AI. ✦ AI generated
Thierry Pienaar · The TWIML AI Podcast · 2026-07-02 · original ↗
starts at this moment · 1:41
“Thierry, how does that jive with the way you think about it? HPE's historically had a private cloud AI offering. You've got a AI factory offering. Are those the same? Are they different?”
If you think about the scale, the maturity of different customers, we have customers that need sixteen GPUs, and they need to do generative AI, and they're just getting onto their journey. We have other customers that have been doing HPC for twenty, thirty years and understand traditional HPC environments. Now they're moving into AI and using AI to transform HPC, but they're highly sophisticated.
verbatim transcript · starts at 1:41
1:41maturity of different customers, we have customers that need sixteen GPUs, and they need to do generative AI, and they're just getting onto their journey. We have other customers that have been doing HPC for twenty, thirty years and understand traditional HPC environments. Now they're moving into AI and using AI to transform HPC, but they're highly sophisticated. So the concept of AI factory, can move along this medium, in
2:03terms of need and sophistication. if we look back a couple of years, training huge models was the big driver, right? the frontier labs, would largely work with the hyperscalers and contract, many s- servers and GPUs to do training workloads. More recently, though, inferences kinda come to the forefront as the driver for, enterprise and really, creating these new opportunities for neo clouds and others. talk a little bit about what you're seeing
2:37on the inference side among your customers. You have to look at inference from a cost optimization perspective, power to token, and then also from a, let's say, architectural strategy perspective. So you can see the adoption of inference with technologies like memory optimizations, like KB Cache, CXL, intelligent routing capabilities like Dynamo from NVIDIA as well And so the ability to optimize, from the request all the way to the
3:06token, is load balancing cost fit for purpose for that workload in terms of the type of GPU, the type of CPU, or the type of LPU you're using in combination, and the type of memory strategies. it's one thing to use AI via an external service provider. It's a totally different, ball of wax to build out that infrastructure within your organization. What challenges are you seeing for the organizations that take this on?
3:35I think the challenges are, so uniform, so it starts with data as an example, right? So AI is really not meaningful without data that is good data, right? garbage in, garbage out type of thing. So- That's your context. Exactly. So I think that's still a huge challenge for organizations is, one is mapping the data, just knowing where the data is, what format is it in, what sequence is